Input mixup [1]} can be viewed as an example of VRM, and it turns out that NFM can be constructed within a VRM framework at the feature level (see Section  in SM). On a high level, NFM can be interpreted as a random procedure that introduces feature-dependent noise into the layers of the deep neural network. Since the noise injections are applied only during training and not inference, NFM is an instance of a stochastic learning strategy. Note that the injection strategy of NFM differs from those of [2]}, [3]}, [4]}. Here, the structure of the injected noise differs from iteration to iteration (based on the layer chosen) and depends on the training data in a different way. We expect NFM to amplify the benefits of training using either noise injection or mixup alone, as will be shown next.
